The topic of presentation is Machine Learning for No-Reference (Blind) Video Quality MOS and Distortion Measurements
Detecting and scoring (MOS) video quality based on packet loss or partial decoding of the stream to determine how much quantization was done at the encoder can only score the last encode and is not sensitive to blur or other defects in the uncompressed source. Also, most No-Reference (NR) video quality analyzers that do decode and analyze each frame search for signatures of specific defects such as filtering to detect the loss of detail or blur or block edges due to tiling. The results of these measurements need to be pooled to create a useful NR MOS score. What is needed is a Machine Learning approach that is trained on very large (big data) data base of images with various levels and types of distortion, pre-scored with established Full-Reference (FR) methods like MS-SSIM, that learns how to map a set of features into an estimated NR MOS score that matches the FR score. This way no reference images are needed and a Blind, NR MOS scoring method can be used over a wide range and combinations of image distortions.
Speaker Bio: Dan Baker has a B.S. and M.S. in Electrical Engineering with current academic studies in Advanced Digital Signal Processing and Machine Learning applied to Image Processing. He is a long time member of both SMPTE and IEEE providing technical support for numerous video standards including contributions to DVB and EBU. He co-authored two papers published in the SMPTE Journal and has over 40 issued patents in video signal and image processing with several pending. He is currently working on new HDR/WCG measurement methods as well as a No-reference Video Quality measurement.
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